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150 lines
3.1 KiB
ReStructuredText
150 lines
3.1 KiB
ReStructuredText
Object Detection
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================
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.. highlight:: c
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.. index:: MatchTemplate
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.. _MatchTemplate:
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MatchTemplate
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-------------
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`id=0.133207508798 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/c/imgproc/MatchTemplate>`__
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.. cfunction:: void cvMatchTemplate( const CvArr* image, const CvArr* templ, CvArr* result, int method )
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Compares a template against overlapped image regions.
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:param image: Image where the search is running; should be 8-bit or 32-bit floating-point
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:param templ: Searched template; must be not greater than the source image and the same data type as the image
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:param result: A map of comparison results; single-channel 32-bit floating-point.
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If ``image`` is :math:`W \times H` and ``templ`` is :math:`w \times h` then ``result`` must be :math:`(W-w+1) \times (H-h+1)`
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:param method: Specifies the way the template must be compared with the image regions (see below)
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The function is similar to
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:ref:`CalcBackProjectPatch`
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. It slides through
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``image``
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, compares the
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overlapped patches of size
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:math:`w \times h`
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against
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``templ``
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using the specified method and stores the comparison results to
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``result``
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. Here are the formulas for the different comparison
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methods one may use (
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:math:`I`
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denotes
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``image``
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,
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:math:`T`
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``template``
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,
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:math:`R`
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``result``
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). The summation is done over template and/or the
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image patch:
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:math:`x' = 0...w-1, y' = 0...h-1`
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* method=CV\_TM\_SQDIFF
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.. math::
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R(x,y)= \sum _{x',y'} (T(x',y')-I(x+x',y+y'))^2
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* method=CV\_TM\_SQDIFF\_NORMED
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.. math::
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R(x,y)= \frac{\sum_{x',y'} (T(x',y')-I(x+x',y+y'))^2}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}
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* method=CV\_TM\_CCORR
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.. math::
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R(x,y)= \sum _{x',y'} (T(x',y') \cdot I(x+x',y+y'))
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* method=CV\_TM\_CCORR\_NORMED
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.. math::
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R(x,y)= \frac{\sum_{x',y'} (T(x',y') \cdot I'(x+x',y+y'))}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}
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* method=CV\_TM\_CCOEFF
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.. math::
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R(x,y)= \sum _{x',y'} (T'(x',y') \cdot I(x+x',y+y'))
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where
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.. math::
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\begin{array}{l} T'(x',y')=T(x',y') - 1/(w \cdot h) \cdot \sum _{x'',y''} T(x'',y'') \\ I'(x+x',y+y')=I(x+x',y+y') - 1/(w \cdot h) \cdot \sum _{x'',y''} I(x+x'',y+y'') \end{array}
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* method=CV\_TM\_CCOEFF\_NORMED
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.. math::
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R(x,y)= \frac{ \sum_{x',y'} (T'(x',y') \cdot I'(x+x',y+y')) }{ \sqrt{\sum_{x',y'}T'(x',y')^2 \cdot \sum_{x',y'} I'(x+x',y+y')^2} }
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After the function finishes the comparison, the best matches can be found as global minimums (
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``CV_TM_SQDIFF``
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) or maximums (
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``CV_TM_CCORR``
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and
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``CV_TM_CCOEFF``
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) using the
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:ref:`MinMaxLoc`
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function. In the case of a color image, template summation in the numerator and each sum in the denominator is done over all of the channels (and separate mean values are used for each channel).
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